Data By the Bay is the first Data Grid conference matrix with 6 vertical application areas spanned by multiple horizontal data pipelines, platforms, and algorithms. We are unifying data science and data engineering, showing what really works to run businesses at scale.

Sign up or log in to save this to your schedule and see who's attending!

Deep learning has shown breakthrough results in computer vision, speech recognition, and natural language processing, but as yet, the applications to medicine are few. Now that we've thoroughly honed our techniques to detect cats in YouTube videos, can we apply that same technology to save lives?

We'll report on a collaboration between a team of cardiologists at UCSF and machine learning engineers at Cardiogram, using sensor data from Apple Watch and other wearables to prevent strokes. About a quarter of strokes are caused by atrial fibrillation, the most common heart arrhythmia. In atrial fibrillation, electrical conduction in the heart becomes disorganized. The upper chambers may beat 300-600 times per minute. The lower chambers may beat at a normal rate, but irregularly. AF is treatable, but asymptomatic—many people don't realize they have it—and if you can develop an algorithm to detect when a person has entered an episode of atrial fibrillation using heart rate time series, you can potentially prevent a stroke.

The talk will include a brief introduction to cardiac electrophysiology, a review of key techniques in deep learning, and then dive into how we're using convolutional autoencoders and semi-supervised sequence learning to detect anomalous patterns of heart rate variability. We'll include lots of example data to build up intuition, and code examples using TensorFlow. Depending on the length of the talk, the algorithmic techniques covered will likely include: convolutional autoencoders for dimensionality reduction, long-short term memory, and semi-supervised sequence learning. (If this is a 20 minute talk, we'll focus on just one of those techniques.)

We'll conclude with some broader thoughts on the intersection of artificial intelligence and medicine, including lessons learned while bridging the cultures of machine learning research and clinical research, as well as some thoughts on how artificial intelligence may drive the future of healthcare.

Brandon currently applies machine learning to cardiology at Cardiogram. Previously, he helped fix healthcare.gov, co-founded Sift Science, and worked as an engineer at Google on Android speech recognition.